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Salt Segmentation with Fully Convolutional Networks and Transfer LearningNormal access

Authors: P. M. Cruz and J. P. Navarro
Event name: First EAGE Workshop on High Performance Computing for Upstream in Latin America
Session: ML/DL/CLOUD
Publication date: 21 September 2018
DOI: 10.3997/2214-4609.201803083
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 989.97Kb )
Price: € 20

In this work we present a new methodology for segmenting salt structures in seismic images, the proposed method is based on Deep Learning. Salt segmentation on seismic data is a challenging task due to several aspects. In general, the salt structures have a very complex geometrical shape, hence is more complicated to define an a-priori geometric model than in layered environments. The high impedance contrast at vicinity of salt reduces dramatically the seismic energy propagating through the salt bodies. This makes difficult to illuminate pre-salt targets, and to correctly model salt boundaries. In modern interpretative processing workflows salt modeling is one key aspect to effectively produce accurate seismic images near the salt. The main object of this research is to develop an automatic salt segmentation solution. The methodology of this solution is based on Deep Learning (DL), Fully Convolutional Networks (FCN), and Transfer Learning (ML). Results are presented in real seismic data.

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